AI as an enabler

AI’s enabling power spans diverse applications and fields. Unlike traditional technologies, AI possesses the ability to learn, adapt, and perform tasks that typically require human intelligence, making it a versatile tool for organisations seeking to stay competitive in a rapidly evolving market.

AI fosters collaboration between humans and machines, augmenting human abilities and enabling new forms of interaction and problem-solving. This synergy can democratise access to information and resources, empowering organisations with limited capabilities to achieve their missions more effectively. In sectors such as healthcare, education and not-for-profit, AI can bridge gaps and provide tailored solutions that were previously unattainable. Laborious time-consuming and repetitive activities can now be replaced with further human creativity and innovation.

AI is a complementary tool to assist in human labour that will augment human activities to higher value-add activities.

Recognising AI as an enabler involves understanding its strategic importance, the necessity for robust implementation frameworks and the ethical considerations essential for responsible deployment. AI still requires human supervision and judgement. By leveraging AI’s capabilities, organisations can enhance their operational efficiencies and drive meaningful societal impact. This ensures sustainable and inclusive growth in the digital age, fostering innovation while addressing global challenges.

Building the business case and value proposition for AI

Establishing a compelling business case for AI involves demonstrating its potential to deliver significant value across various organisational dimensions, including enhancing operational efficiencies, improving decision-making processes and fostering innovation.

expert tip:

Assess the strategic importance and imperative of AI adoption across the business and the broader industry.

Strategic importance and urgency

Business leaders are increasingly recognising the strategic importance of AI in maintaining a competitive edge and in fostering innovation. This strategic move is driven by an era of hyper-personalisation that aims to tailor products and services to better meet consumer needs, along with enhanced supply chain integrity and transparency to achieve ESG goals and meet stakeholder expectations.

AI’s role goes beyond just automating tasks. It enhances the agility of an organisation, allowing it to respond more swiftly to market forces, including evolving consumer needs.

AI plays a crucial role in the development of both current employees and the future workforce by integrating advanced technologies into educational programs and workplace software applications. These initiatives not only prepare individuals for a digital future but also ensure that organisations are continually revitalised with fresh ideas and new approaches to problem-solving. To effectively harness AI’s potential, however, organisations should adopt an incremental integration approach. This involves facilitating careful evaluation and governance, with a realistic understanding of AI’s capabilities and limitations so its application is both ethical and effective. 

Enhancing operational efficiency and security

AI’s transformative capabilities are evident in sectors like healthcare. In healthcare, AI tools play a pivotal role by analysing vast amounts of patient data to forecast health outcomes and tailor treatment plans to individual needs. This not only boosts the efficiency of healthcare delivery but also elevates the quality of patient care. 

In the realm of security, AI-enhanced surveillance systems are instrumental. These systems scrutinise behaviour patterns to detect anomalies and promptly alert human operators, thereby averting potential security threats before they escalate. Through these innovations, AI is not just streamlining operations but is also bolstering safety and security across diverse fields.

Accelerating startup development

Startups, especially in the technology sector, are leveraging AI to accelerate their development processes and enhance market analysis. By incorporating AI, these nimble businesses can automate significant portions of manual processes. This automation allows businesses to allocate more time and attention to other aspects, such as refining design and user experience, which can differentiate a product in competitive markets. The use of AI not only fast-tracks the product development cycle but also contributes to substantial reductions in labour costs. These efficiencies are crucial for startups, where speed and cost-effectiveness are often paramount to success. The speed of market access and penetration is the new competitive advantage.

expert tip:

Leverage AI tools to enhance the monitoring and mitigating security of digital assets and online environments.

Startups are demonstrating a strong willingness and a greater risk appetite when it comes to adopting AI. AI helps them get products to market faster – usually half the time and half the cost. Michelle Moffatt

Facilitating sustainable practices and economic efficiency

In sectors such as advanced manufacturing, AI is becoming a key player in promoting sustainability and enhancing economic efficiency. AI enables companies to optimise their energy consumption and minimise waste through smarter management of resources. For example, AI can help with predictive maintenance, which not only prevents unexpected equipment failures but also extends the lifespan of the machinery, conserving resources and reducing waste. AI can help fine-tune supply chain operations, ensuring that materials and products are moved more efficiently, further cutting down on excess and inefficiency. Together, these applications of AI support a more sustainable manufacturing process, aligning operational goals with environmental stewardship.

Generative AI: lowering barriers and encouraging innovation

Generative AI represents a significant evolution towards more advanced AI systems. Unlike its predecessors, generative AI can create new content, from textual outputs to images and code, demonstrating a leap in AI’s creative and functional capabilities. ChatGPT is a prominent example of generative AI, known for producing coherent and contextually relevant text in a conversational style. It is an example of the democratisation of AI.

This capability has broadened the scope of AI applications in everyday tasks and business operations.

In parallel, APIs provide accessible and streamlined access to powerful generative AI models, enabling developers and businesses to easily integrate these capabilities into their own applications. This empowers even small businesses and individual entrepreneurs, who can now deploy advanced AI technologies without the need for extensive resources or deep technical expertise. By making these tools widely accessible, generative AI enhances creative processes and efficiency, fostering innovation and providing a competitive edge across various industries. 

Small business operators should still seek to understand how AI may affect their business decisions, legal compliance and operations. Some particular considerations for creators and innovators include:

• Ownership of content: Copyright generally does not subsist in AI-generated works in Australia because copyright law requires there to be a human author. So, creators may not own the copyright in their content if it was generated by AI. Others may then use that content without infringing copyright. Similarly, patent law requires a human inventor, so an invention generated by AI may not be patentable.
• Liability for output of an AI: AI may produce output that is inaccurate, unreliable, misleading, biased, defamatory or otherwise inappropriate. The risk increases where AI is used to make decisions or there is no effective human review. The fact that AI produced the output is unlikely to be a legal defence.
• Use of third party data: Organisations should ensure they have appropriate rights (from a copyright, confidentiality and privacy perspective) before providing data to an AI system.
• Protecting data from training: AI systems are often trained on a large quantity of data scraped from the internet. Organisations should consider if they are comfortable with their data being viewed by AI models and, if not, taking technical or legal steps to prevent that from occurring.

The barrier for adoption has really come down…the cost to get going with experiments and using the technologies is very low, the amount of skill required to engage with the technologies also just dropped away. Chris Burling

Open-source AI: opportunities and challenges

Open-source AI has revolutionised the way organisations, from startups to multinational corporations, access and use advanced technology. However, while it provides transformative opportunities for technological advancement and collaborative innovation, effectively navigating its landscape demands a deeper understanding of both its benefits and
inherent risks.

By making AI models and frameworks publicly available, it enables a global community of developers to innovate and accelerate technological advancements. For instance, open sourcing pre-trained AI models empowers a global community of developers to innovate and tailor these models to their specific needs through fine-tuning. This approach not only democratises access to cutting-edge technology but also accelerates the pace of innovation across and between various sectors, enabling customised solutions that address unique challenges and enhance competitive advantages.

However, the rapid development cycle of using opensource models, while advantageous, presents its own set of challenges when compared to developing and deploying proprietary systems.

EXPERT TIP:

Prioritise the robustness of data privacy, cyber security and backup systems in the initial stages of AI technology adoption to safeguard against immediate and long-term operational risks.

Open-source AI allows for quicker adaptation and integration, offering a significant edge over slower, closed-source developments from scratch. Yet, this can sometimes result in less rigorous testing and variability in quality. Organisations must balance the speed of adoption with thorough validation processes to ensure reliability.

Startups face distinct challenges when integrating open-source AI, particularly due to their reliance on external AI services which may experience interruptions. To mitigate such vulnerabilities, it is advisable for startups to develop diversified technology strategies. These should encompass a variety of open-source options, alongside alternative solutions and comprehensive backup systems. Drawing on insights from the volatile crypto industry, it is essential for startups to prioritise robustness and data privacy from the initial stages of technology adoption. This approach not only safeguards against immediate operational risks but also establishes a foundation for long-term resilience and sustained growth.

Organisational readiness for AI

Getting an organisation ready for AI will help harness the full potential of AI while aligning its use with broader strategic objectives.

Technological infrastructure

Successful AI adoption requires a robust infrastructure tailored to the demands of AI technologies. Organisations must assess their current systems to identify gaps, focusing on enhancing data processing capabilities and security measures. Upgrading hardware and implementing advanced encryption and automated bias detection tools are essential for responsible AI practices. Alternatively, organisations can leverage cloud-based services, which offer scalable and cost-effective solutions for AI deployment without the need for extensive on-site upgrades. This approach simplifies the integration of advanced AI technologies and ensures compliance with evolving data protection and privacy standards.

Workforce preparedness

Preparing the workforce for AI integration involves equipping employees with the technical skills necessary for working with AI technologies and a deep understanding of responsible AI.

Continuous education and training programs are essential for developing AI literacy across the organisation.

These programs should focus on fostering a culture of responsible innovation, ensuring employees are proficient in using AI tools while understanding the ethical considerations and best practices for deploying AI responsibly.

EXPERT TIP:

Align AI training with existing digital literacy training programs such as cyber security training to uplift capabilities across the organisation.

Cultural readiness and leadership

Fostering a culture that promotes innovation, agility and responsible AI practices is essential for successful AI integration. Organisations that encourage collaboration, experimentation and continuous improvement are better equipped to implement AI effectively. Strong leadership plays a crucial role in this process by championing AI initiatives and establishing governance frameworks that cover risk management and ethical considerations. Embedding responsible AI principles into the culture ensures that ethical considerations are central to AI projects, enhancing both productivity and ethical standards. Leaders must ensure that these values are upheld throughout the organisation, guiding teams toward ethical and innovative AI solutions.

EXPERT TIP:

Align AI training with existing digital literacy training programs such as cyber security training to uplift capabilities across the organisation.

Figure 3: 

Driving cultural change across the organisation.

For any assistance with addressing the AI governance needs of your organisation, do not hesitate to contact your local PKF Audit and governance expert.